Learning Analytics: Making learning better?
Slides for a talk at Bett 2015, London, Fri 23 January, as part of the LACE project (www.laceproject.eu)
This panel discussion starts with a short introduction to learning analytics and educational data mining, highlighting how European schools are using different types of data to help support, manage and predict learning outcomes. It includes viewpoints from national school networks in the Nordic countries and the Netherlands, a research input from the European Commission supported LACE project highlighting research on the use of learning analytics and an expert input on ethical and privacy issues in the application of learning analytics. Participants will be encouraged to share their views and where interested to join the growing LACE Community
1. Learning Analytics:
Making learning better?
Dr Doug Clow
Institute of Educational Technology, The Open University, UK
@dougclow
dougclow.org
doug.clow@open.ac.uk
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3. What is learning analytics?
• the measurement, collection, analysis and reporting of
data about learners and their contexts, for purposes of
understanding and optimising learning and the
environments in which it occurs
– First International Conference on Learning Analytics And Knowledge (LAK11), Banff, Alberta, Feb 27-
Mar 1, 2011
Photo (CC)-BY Cris: http://flickr.com/photos/chrismatos/6917786197/
4. Photo public domain: http://commons.wikimedia.org/wiki/File:DESYNebelkammer.jpg
- Erik Duval
http://erikduval.wordpress.com/2012/01/30/learning-
analytics-and-educational-data-mining/
“collecting traces
that learners leave
behind and using
those traces to
improve learning”
5. “feeding back the
data exhaust”
Big Data in
Education
Photo (CC)-BY Iain Watson http://www.flickr.com/photos/dagoaty/3329699788/
12. What data do we have about learners?
• Demographics
• Previous educational experience
• Grades, scores, achievements, struggles
• Attendance, location, gaze
• Software logs
• Online tracking
• Other online activity (tracking)
• … more every week.
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Photo (CC)-BY-SA AJ Cann https://www.flickr.com/photos/ajc1/15574010080/
13. What can we do with that data?
• Identify learners who need help
– Simple or predictive
• Trigger interventions
– Via teacher, or direct
• Learn which interventions work
• Build a complete cognitive learning system
• Suggest resources or source of help
– Learners like you found this helpful
– This person might be able to help you
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Photo (CC)-BY-NC Pulpolux https://www.flickr.com/photos/pulpolux/8735428280
17. Glasswinged butterfly, ? Greta oro
Photo (CC)-BY-NC-ND by Greg Foster on Flickr http://www.flickr.com/photos/gregfoster/3365801458/
Principles
• Privacy
• Data
protection
• Ethics
• Transparency
19. Thanks to:
People:
• LACE at the OU: Rebecca Ferguson, Simon Cross, Linda Norwood Michelle
Bailey, Rebecca Wilson, Evaghn De Souza, Natalie Eggleston, Oliver
Millard, Gary Elliot-Citigottis,
• LACE project partners: CETIS (Bolton), OUNL, Skolverket, HIOA,
Kennisnet, ITS, ATiT.
• The learning analytics community, including SoLAR, IEDMS, those I’ve met
at LAK and LASI
Funders:
• LACE: European Commission 619424-FP7-ICT-2013-11
20. “Learning Analytics: Making Learning Better?” by Doug Clow,
Institute of Educational Technology, The Open University,
was presented at Bett, London, on 23 January 2015.
@dougclow
dougclow.org
doug.clow@open.ac.uk
This work was undertaken as part of the LACE Project, supported by the European Commission Seventh
Framework Programme, grant 619424.
These slides are provided under the Creative Commons Attribution Licence:
http://creativecommons.org/licenses/by/4.0/. Some images used may have different licence terms.
www.laceproject.eu
@laceproject
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21. cc licensed ( BY ) flickr photo by David Goehring: http://flickr.com/photos/carbonnyc/33413040/
Editor's Notes
Please copy, adapt, photograph, video. Tell your friends!
Learning analytics is on the Internet and the Internet has cats
Data mining, business intelligence, academic analytics, learner analytics – focus here is on the learning, not the management and administration of learning
Photo: Cloud Chamber at the German Electron Synchrotron DESY
Our data isn’t big. Most fits in Excel!
Small data = Excel, Medium = laptop with R or other stats, Big = need special servers/cloud services
Without interventions: still good stuff: computer science, educational research, business intelligence
But only LA if fed back.
What good teachers have always been doing, but more data, and better techniques.
Massive investment by educational software vendors, from VC-backed startups to large international media companies.
Most products here at Bett will have a dashboard or analytics.
Cohort dispositional analytics.
Building critical self-awareness.
Correlations with success measures, but complex relationship.
Learning power goes down over time in school!
To help the learner! Not just tracking.
Privacy – education makes space to fail, make mistakes, and learn from them – and not have that held against you.
Data protection – longstanding EU legislationEthics vast, complex, tricky. 70% chance to complete (about right) but 1% chance succeed.Alex the student.
Vs ignoring info could help success.
When does keeping the door to success open become giving unrealistic hope?
Ethics critical path. Transparency – to learners, but to the outside. Shared processes.